Data Mining: Concepts, Models, Methods, and Algorithms, 2nd Edition

Description

This book reviews state-of-the-art methodologies and techniques for analyzing enormous quantities of raw data in high-dimensional data spaces, to extract new information for decision making. The goal of this book is to provide a single introductory source, organized in a systematic way, in which we could direct the readers in analysis of large data sets, through the explanation of basic concepts, models and methodologies developed in recent decades.

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About the Author

MEHMED KANTARDZIC, PhD, is a professor in the Department of Computer Engineering and Computer Science (CECS) in the Speed School of Engineering at the University of Louisville, Director of CECS Graduate Studies, as well as Director of the Data Mining Lab. A member of IEEE, ISCA, and SPIE, Dr. Kantardzic has won awards for several of his papers, has been published in numerous referred journals, and has been an invited presenter at various conferences. He has also been a contributor to numerous books.

DBSCAN clustering algorithm – as a representative of an important class of density-based clustering methodologies

Temporal and Spatial Data Mining – including streaming data analyses is an important trend in data mining recognizing value of time and space information in real world applications

Web and Text Mining

Parallel and Distributed Data Mining

Updates on the older techniques presented in the first edition

“I therefore gladly salute the second editing of this lovely and valuable book. Researchers, students as well as industry professionals can find the reasons, means and practice to make use of essential data mining methodologies to help their interests.” (Zentralblatt MATH, 2012)